Open AccessDeep learning-based defect detection for hot-rolled strip steel
Author(s) -
Binglong Si,
Musha Yasengjiang,
Huawen Wu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2246/1/012073
Subject(s) - pooling , layer (electronics) , feature (linguistics) , artificial intelligence , surface (topology) , computer science , transfer of learning , planar , feature extraction , pattern recognition (psychology) , materials science , composite material , mathematics , geometry , computer graphics (images) , linguistics , philosophy
Defects in the strip’s surface can have an impact on the strip company’s product sales. As a result, it’s crucial to spot flaws on the strip steel’s surface. The Faster R-CNN network is structurally upgraded for the issue of strip steel surface defect detection by employing FPN for feature fusion in the feature extraction layer, RoI Align instead of RoI Pooling in the pooling layer, and Softer-NMS in the final prediction fully connected layer. In addition, for training, transfer learning is employed. The experimental findings demonstrate that the suggested technique mAP outperforms the original Faster R-CNN by 7.6%, and the detection time is fast enough for strip steel detection online.